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Probing the axioms of evolutionary algorithm design: Commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin

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A Reply to this article was published on 24 February 2017

The Original Article was published on 23 February 2017

Abstract

Properties such as continuity, locality, and modularity may seem necessary when designing representations and variation operators for evolutionary algorithms, but a closer look at what happens when evolutionary algorithms perform well reveals counterexamples to such schemes. Moreover, these variational properties can themselves evolve in sufficiently complex open-ended systems. These properties of evolutionary algorithms remain very much open questions.

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Acknowledgements

This work was supported by the Konrad Lorenz Institute for Evolution and Cognition Research, the Mathematical Biosciences Institute through National Science Foundation Award #DMS 0931642, the University of Hawai‘i at Mānoa, and the Stanford Center for Computational, Evolutionary and Human Genomics, Stanford University. I thank Marcus W. Feldman for his hospitality during a visit to his group.

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Altenberg, L. Probing the axioms of evolutionary algorithm design: Commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin. Genet Program Evolvable Mach 18, 363–367 (2017). https://doi.org/10.1007/s10710-017-9290-3

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